Study On Fuzzy Neural Network Model Of The Vehicle-bicycle Conflicts At Grade Intersection Based On Failure FTA Theory

Mixed trffic flow, which is the main characteristic of large cities in China, is regarded as one of the primary reasons for serious traffic congestion at grade intersection. Especially, the interference between the through bicycles and the right-turn motors on adjacent lane happen easily. If the conflict happens, the traffic apacity and the security is to be influenced. Accordingly the paper proceeds from influencing factor, it analyzes the critical factor. The main aim is to reduce the interference between the through bicycles and the right-turn motors on adjacent lane.The paper collects data through video for nine imports of three intersections in the city of Beijing, and based on the data analysis from video analyzing and validating the correlative theory of traffic interference. Firstly, based on the TCT, according to the conflict amount and the danger degree, the vehicle-bicycle conflicts at the Gan Jiakou intersection are classified into 3 groups using the fuzzy clustering theory, and the most serious interference point that is the interference between the through bicycles and the right-turn motors on adjacent lane is used as the case study. In terms of running qualities of a wagon, act in violation of regulation and the composite factor, the failure tree model of bicycle traffic safety is built according to the safety engineering and the main reasons of vehicle-bicycle conflict are explained from two aspects: the minimal cut set and the structural importance. Secondly, based on the failure tree analysis results, and taking the minimal cut set as the input variable the fuzzy neural network model of vehicle-bicycle conflict at grade intersection is built. Using Matlab R2008a, the paper prognosticates the relation beween the reasons and the amount of conflict; the analysis results show that the reasons causing bicycle traffic conflict at grade intersection can be well analyzed and evaluated using the fuzzy neural network model. The result not only promote requirement for the improvement of the safety of groad intersection but also provide accordance to improving the grade intersection.